TY - JOUR
T1 - Synergistic tomographic image reconstruction
T2 - Part 2
AU - Tsoumpas, Charalampos
AU - Sauer Jørgensen, Jakob
AU - Kolbitsch, Christoph
AU - Thielemans, Kris
N1 - Funding Information:
In order to bring together scientists and academics across different research domains, we organized in November 2019 a symposium on Synergistic Image Reconstruction (http://www.ccpsynerbi.ac.uk/symposium2019) and this special issue includes work presented at this event. The meeting was sponsored by two EPSRC-sponsored networks: the collaborative computational project (CCP) in synergistic PET-MR image reconstruction (CCP-PET-MR, http://www.ccppetmr.ac.uk) and the collaborative computational project in tomographic imaging (CCPi, https://www.ccpi.ac.uk/). Both these networks, which will stay active at least until 2025, with the former expanded to synergistic reconstruction in biomedical imaging (CCP SyneRBI, https://www.ccpsynerbi.ac.uk/), welcome national and international collaborations to better support their scientific communities.
Publisher Copyright:
© 2021 The Authors.
PY - 2021/8/23
Y1 - 2021/8/23
N2 - This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
AB - This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
KW - diffuse optical tomography
KW - magnetic resonance imaging
KW - open-source software
KW - positron emission tomography
KW - tomography
KW - X-ray computed tomography
U2 - 10.1098/rsta.2021.0111
DO - 10.1098/rsta.2021.0111
M3 - Article
C2 - 34218672
AN - SCOPUS:85110826625
SN - 1364-503X
VL - 379
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
IS - 2204
M1 - 20210111
ER -